Falmouth Field Course 2017

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Phosphate:

The phosphate results are again very similar to the nitrate and silicon results; as depth increases, phosphate also increases. This is again linked to phytoplankton uptake, but phosphate differentiates in that it is a magnitude of 10-1 smaller than silicon and nitrate. The plankton need phosphate in order to build important biological components such as nucleic acids and nucleotides, and thus are vital for processes such as photosynthesis itself. The maximum and minimum values of phosphate present over all stations were again at station C39, going from 0.035 umol/l at the surface to 0.249 at depth. As well as the CDM influencing this, C39’s nutrient structure could also be due to its strong stratification which results from it being the most offshore station where it is deeper, as this traps the nutrients in either the upper or lower water bodies.

Silicon:

The graph shows that in most of the sampled stations, silicon concentrations were lowest at the surface and increased with depth. This trend can be explained when compared with the chlorophyll concentration results, which show an inverse relation with depth; where chlorophyll is greatest in the surface waters, silicon concentration is lowest. It is likely that phytoplankton are taking up the silicon from the water, depleting it is the surface. The plankton use this silicon by incorporating it into the outer skeletons ie the frustules of diatom species. Deeper in the water column where phytoplankton are less plentiful, the nutricline can recover. The exception to this trend is station C43 which slightly decreases with depth, although his deep water value was actually very shallow relative to the other station data points, and so could be considered still in shallow water. The data point could also simply be anomalous. The maximum silicon value was 1.84 umol/l, present at depth at station C39. The minimum was the surface concentration of C39, at 0.0 umol/l (only trace amounts). The reason for this very low value could be due to the presence of the DCM at station C39, as discussed in the chlorophyll results.

Nitrate:

Much like the silicon graph, nitrate also shows a trend of increasing with depth. This is likely due to the similar forcing factors – high chlorophyll and thus plankton in the surface waters, which take up nitrate in order to synthesise amino acids which are needed for protein building. The station C39 deep water value point had the maximum nitrate value at 1.64 umol/l, as well as its surface water having the minimum value which was 1.26 umol/l. This low point is again likely linked to the DCM at C39.

Figure 2. Nitrate Profiles

Figure 3. Phosphate Profiles

Figure 1.  Silicon Profiles

Chlorophyll:

It is predicted that under normal conditions, chlorophyll would be greatest at the surface where phytoplankton would have the most access to solar irradiance, as this is beneficial for photosynthesis. The data and graph clearly display this; as depth increases, chlorophyll concentrations decrease. The exception for this is station c39, which slightly increases at around 25m before decreasing as normal. This could indicate the presence of a deep chlorophyll maximum (DCM). DCM’s most commonly form in late summer, when the ocean is heavily stratified and little mixing of nutrients take place. Under these conditions, the optimal section of the water column (ie the most sunlight at the depth with enough nutrients to sustain normal functions) is on the thermocline, which exists below the surface waters. This explains why station C39 has this increase. The maximum value of chlorophyll over all of the stations was not C39 however, but the surface value for C43 at 3.17 umol/l. The minimum chlorophyll value was station C39’s deep value, 1.57 umol/l, perhaps because the small amount of plankton that would have been present here is further reduced by the DCM, and consequently the plankton are less spread out.

Figure 4. Chlorophyll Profiles

Dissolved Oxygen:

Unfortunately, the samples collected on board Callista for oxygen analysis leaked during transit from the boat to the labs. Consequently, the values obtained using the Winkler Tritation method are likely incorrect and not representative of the stations. This explains why the oxygen saturation results are so low, and therefore data analysis are impossible to carry out.

Figure 5.  Dissolved Oxygen Profiles

Chemistry

Summary:

In conclusion, whilst chlorophyll decreased with depth, silicon, nitrate and phosphate all increased with depth. Station 39 was consistently the most nutrient poor at the surface, and nutrient rich at depth. This is explained by relating it to the chlorophyll data, which displays a deep chlorophyll maximum at 25m depth; as plankton had already used up the nutrients in the surface waters, a DCM had formed further down the nutricline, on top of the thermocline. This means most plankton at the station were in the DCM and not at depth, leading to the lowest recorded chlorophyll value at deep water. This allowed recovery of the nutricline and thus the very nutrient rich bottom waters of station 39. Because station C39 was the furthest offshore, it was also deepest and consequently more highly stratified. In these conditions, plankton blooms are more likely to occur in the CDM. In comparison, the other stations were closer to shore and so were shallower as well as the tide having a greater influence on water structure, leading to a more mixed profile. Under these conditions, blooms are found at the surface within the boundary region of the coastal front where there is optimal light and nutrients. These contrasting environments also help explain the chlorophyll, and consequently nutrient, distribution.

Intro

In order to examine the chemical properties offshore of the Fal estuary, samples were taken on the 11th of July 2017, from 5 stations using a CTD rosette with 6 Niskin bottles onboard the RV Callista. This allowed the identification of chemical trends and mixing patterns throughout the water column.

Limitations

The locations at which the sampling took place was largely dictated by the weather as many regions experienced undesirable conditions. This reduced the area available for sampling, which may limit the data’s full representation of the offshore environment.